Network Component Parameter Inference via Bayesian Optimization
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Solution Overview
Problem
Existing network tomography techniques are inapplicable to most real networks due to structural limitations in network topologies, which make it impractical to achieve full monitoring coverage with a sufficient number of probing paths, limiting the ability to infer internal network node parameters.
Innovation Solution
A method that identifies multiple paths through the network, measures path parameters, generates constraints by expressing these values as functions of component parameters, and uses optimization and statistical distributions to estimate component parameters, including noise sensitivity analysis and error correction, allowing for inference of component parameters even in unidentifiable networks.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If traditional network tomography techniques are applied to infer internal network parameters, then parameter inference capability is improved, but the method becomes inapplicable to majority of real networks due to structural limitations
Solution Approach 1:
The patent changes the fundamental parameters of the network tomography approach by transitioning from requiring full observability (traditional method) to working with partial observability. It introduces new parameters including prior distributions for network parameters, likelihood functions based on observed data, and uses Bayesian inference to compute posterior distributions, enabling parameter inference in previously unidentifiable network configurations
Solution Approach 2:
The patent introduces an intermediary statistical modeling layer that connects observed network performance data to unobservable internal parameters. This intermediary layer uses probability distributions and Bayesian inference mechanisms to bridge the gap between limited observations and complete parameter inference, making the system adaptable to real network structures
2Measurement precision
If a sufficient number of probing paths are deployed to achieve full monitoring coverage, then parameter inference accuracy is improved, but the complexity and cost of monitoring infrastructure increases significantly
Solution Approach 1:
The patent applies partial action by demonstrating that full monitoring coverage is not necessary for effective parameter inference. By using Bayesian inference with prior distributions, the system can accurately infer parameters even with limited probing paths, performing only the necessary measurements rather than exhaustive monitoring of all possible paths
Solution Approach 2:
The patent substitutes the mechanical approach of deploying extensive physical monitoring infrastructure with a statistical/computational approach. Instead of adding more probes and monitoring points, it replaces the physical monitoring complexity with statistical modeling and Bayesian inference computations, significantly reducing infrastructure requirements
3Measurement precision
If additional constraints are manually identified and applied to enable full monitoring coverage, then monitoring coverage is improved, but the solution becomes limited to specific cases and cannot be generalized
Solution Approach 1:
The patent creates a universal Bayesian inference framework that can be applied to any network topology and parameter type without requiring case-specific constraints. The methodology works across different network configurations (topologies, scales, types) by using general probabilistic models and prior distributions, making it broadly applicable rather than limited to specific scenarios
Data Source
AI summary
A method for inferring component parameter values for components in a network is disclosed. The components comprise at least one of network nodes or network links and the method comprises identifying a plurality of paths through the network (100), measuring values of a path parameter for identified paths (410), generating a set of constraints by expressing individual measured path parameter values as a function of component parameter values of the components in the path associated with the measured path parameter value (420a), and generating an estimate of the component parameter values by solving an optimisation problem defined by the generated constraints (420b). The method further comprises, for individual components in the identified paths, matching the generated estimates of the component parameter value to a statistical distribution describing a behaviour of the component parameter (430a), identifying a ratio of central moments of the statistical distribution that demonstrates a sensitivity to noise below a threshold value (430b), and calculating an inferred value of the component parameter from the identified ratio of central moments (430c).